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United States Patent |
5,164,597
|
Lodder
|
November 17, 1992
|
Method and apparatus for detecting microorganisms within a liquid
product in a sealed vial
Abstract
An apparatus and method are provided for the noninvasive and nondestructive
detection of microorganisms within a liquid product contained within a
sealed vial. The apparatus includes a near-IR light source that produces
both incident and reference beams having a wavelength between 800 and 2500
nm and, more preferably, 1100 and 1360 nm. The apparatus also includes an
integrating sphere having incident and reference beam ports and a sample
window opposite the incident beam port. A detector is mounted in the
integrating sphere substantially adjacent the sample window. A
substantially U-shaped mirror is provided to hold the vial. The U-shaped
mirror is of a size substantially corresponding to the diameter of the
vial. In operation, the incident beam is directed through a sample window
so as to enter the vial adjacent a sidewall of the mirror. In this way the
U-shaped mirror reflects the incident beam so that it passes through the
vial three times before returning to the detector. A computer analyzes the
resulting signals from the detector.
Inventors:
|
Lodder; Robert A. (Lexington, KY)
|
Assignee:
|
University of Kentucky Research Foundation (Lexington, KY)
|
Appl. No.:
|
696354 |
Filed:
|
May 6, 1991 |
Current U.S. Class: |
250/341.8; 250/228; 250/574; 356/236; 356/338; 356/341; 435/34; 435/39; 435/288.7 |
Intern'l Class: |
G01N 021/51 |
Field of Search: |
250/341,574,228,343
356/337,338,341,343
|
References Cited
U.S. Patent Documents
3627424 | Dec., 1971 | Dorman et al. | 356/338.
|
3966332 | Jun., 1976 | Knapp et al. | 250/223.
|
4087184 | May., 1978 | Knapp et al. | 250/223.
|
4118625 | Oct., 1978 | Underwood | 250/343.
|
4278887 | Jul., 1981 | Lipshutz et al. | 250/432.
|
4291983 | Sep., 1981 | Kraft et al. | 250/574.
|
4804273 | Feb., 1989 | Tondello et al. | 250/574.
|
4900923 | Feb., 1990 | Gerlinger | 250/228.
|
Primary Examiner: Fields; Carolyn E.
Attorney, Agent or Firm: King & Schickli
Parent Case Text
This application is a continuation-in-part of U.S. patent application Ser.
No. 07/581,823 filed Sep. 12, 1990 which is a continuation-in-part of U.S.
patent application Ser. No. 07/414,799, now abandoned, filed Sep. 29, 1989
.
Claims
I claim:
1. An apparatus for the noninvasive and non destructive detection of a
microorganism with a liquid product contained within a vial, comprising:
a light source for producing an incident beam;
means for directing said incident beam through said vial and liquid
product;
means for reflecting said incident beam through said vial and liquid
product;
means for detecting the reflected incident beam and light scattered by any
microorganisms present in said liquid product; and
means for analyzing said incident beam and light scattered by any
microorganism that is detected.
2. The apparatus set forth in claim 1 wherein said light source produces an
incident beam having a wavelength between 800 and 2500 nm.
3. The apparatus set forth in claim 1, wherein said light source produces
an incident beam having a wavelength between 1100 and 1360 nm.
4. The apparatus set forth in claim 1, wherein said light source also
produces a reference beam that is directed to said detecting means so as
to compensate for noise and source intensity variations.
5. The apparatus set forth in claim 1, further including an integrating
sphere for collecting scattered light and directing said scattered light
toward said detecting means.
6. The apparatus set forth in claim 5, wherein said integrating sphere
includes an incident beam port and a sample window opposite said incident
beam port through which said incident beam is directed at said vial and
said liquid product.
7. The apparatus set forth in claim 6, wherein said detecting means is
mounted within said integrating sphere adjacent said sample window.
8. The apparatus set forth in claim 7, wherein said integrating sphere
further includes a reference beam port.
9. The apparatus set forth in claim 8, wherein said reflecting means
comprises a substantially U-shaped mirror.
10. The apparatus set forth in claim 9, wherein said sample window of said
integrating sphere is positioned along the open end of said substantially
U-shaped mirror.
11. The apparatus set forth in claim 10, wherein said sample window
positioned along the open end of said substantially U-shaped mirror is
also adjacent a sidewall of said substantially U-shaped mirror.
12. A method for the noninvasive and nondestructive detection of a
microorganism within a liquid product contained with a vial, comprising
the steps of:
placing the vial adjacent a mirror;
directing an incident beam of light into said vial and liquid product;
detecting said incident beam of light as well as light scattered by any
microorganism present in the liquid product following reflection by said
mirror; and
analyzing said incident beam and light scattered by any microorganism that
is detected.
13. The method set forth in claim 12, wherein said mirror is substantially
U-shaped and has a cavity sized to receive said vial.
14. The method set forth in claim 13, wherein said incident beam is
directed into said vial through an open end of said substantially U-shaped
mirror adjacent a sidewall of said mirror.
15. The method set forth in claim 12, including reflecting said incident
beam through said vial and liquid product at least twice before detecting.
16. The method set forth in claim 12, including reflecting said incident
beam through said vial and liquid product at least three times before
detecting.
17. The method set forth in claim 12, including steps of identifying
multiple discrete scanning planes through said vial and liquid product and
directing said incident beam through said multiple discrete scanning
planes in order to detect a microorganism in said liquid product.
18. The method set forth in claim 12, including providing a reference beam
to compensate for noise and source-intensity variations.
19. The method set forth in claim 12, wherein said incident beam has a
wavelength between 1100 and 1360 nm.
Description
BACKGROUND OF THE INVENTION
Biotechnology has created a number of new and potentially life-saving
products. Many of these products cannot withstand exposure to the
digestive tract as an oral formulation and must instead be formulated as
injectables. Furthermore, these molecules may not survive terminal
sterilization by autoclaving. In these cases, an aseptic-filling process
is required although it is a less reliable sterilization method, making
detection of unsterile products a necessary task. Conventional
microbiology methods and turbidimetry are currently employed as inspection
techniques to assess sterility. However, these procedures are typically
very time consuming, invasive, and characteristically provide relatively
low sensitivity and as such may not detect low levels of contamination.
In more detail, many drugs must be formulated as parenteral products
(injectables), and delivered in a solution contained in a sterile vial or
intravenous (IV) bag. Maintaining the stability of the drug (preventing
decomposition) and insuring the sterility of the drug (absence of
microbial growth) can be a problem.
Preservative systems and sterilization procedures for parenteral products
must be well monitored (see Henry L. Avallone, J. Parenter. Sci. Technol.
1985, 39(2), 75-79) and tested by validated microbiological methods (see
"Validation of Steam Sterilization Cycles", Technical Monograph No. 1, and
"Validation of Aseptic Filling For Solution Drug Products", Technical
Monograph No. 2, Parenteral Drug Association, Inc., 1980). The typical
method of assuring the sterility of vials and IV bags is to fill them with
the desired product and sterilize the final filled product by autoclaving
(see John Y. Lee, Pharmaceutical Technology 1989, 13(2), 66-72).
Unfortunately, the autoclaving process can also stress fragile molecules
and denature proteins. In such cases, the IV bag or vials are filled
aseptically (under conditions that are as sterile as possible) and
sterilized by filtration with a 0.2 .mu.m filter. The product can then be
used.
Unfortunately, sterility by aseptic filling is not as certain as with
terminal sterilization (autoclaving). It has been estimated that terminal
sterilization by autoclaving results in a sterility assurance level of
10.sup.-6 or better (probability of an unsterile unit), while aseptic
filling generally achieves an assurance level of only 10.sup.-3 or one
contaminated unit per thousand (see Quality Control Reports: The Gold
Sheet, in F-D.C. Reports, Bill Paulson, Ed., 1988, 22(3), 1-6 and Henry L.
Avallone, J. Parenter. Sci. Technol. 1986, 40(2), 56-57). Because of this
difference in sterility assurance levels, the FDA is requiring
manufacturers who produce aseptically-filled products to submit methods
and data justifying why terminal sterilization cannot be used. The
manufacturer must also describe the microbiological monitoring and control
procedures used to assure sterility (see FDA Guideline on Sterile Drug
Products Produced by Aseptic Processing; Food and Drug Administration,
Rockville, Md., July, 1987).
The challenge to the analyst is to determine which product is contaminated
and to prevent its use, assuring that the final occurrence of defective
units is very low. Perhaps the simplest method of assuring product
sterility involves the incubation of an IV bag or vial until any
microorganisms that might be present grow sufficiently numerous that
turbidity develops. The turbidity is then detected by ordinary optical
methods or by visual examination. Also, microscopic examination would
reveal the identity of the contaminating microorganism(s). Unfortunately,
it can take a significant amount of time for turbidity to develop, and
products contaminated with small amounts of microorganisms such as
bacteria, molds, or yeast might not show visible turbidity. Furthermore,
some IV bags or vials are composed of materials that interfere with the
visible detection of turbidity.
U.S. Pat. No. 4,367,041 to Webb teaches a liquid chromatography method
where pure components of a mixture may be separated during chromatography
by measurement of the ratio of absorbance at two wavelengths.
A system for detecting the tampering with capsules using near-infrared
(near-IR) light is described by Robert A. Lodder et al. in Anal. Chem.
1987, 59, 1921-1930. Near-IR methods are commonly applied to the analysis
of aqueous samples, see Robert A. Lodder et al., Appl. Spectrosc. 1988,
42, 518-519 and have been used in the detection of contaminated products,
see Robert A. Lodder et al., Appl. Spectrosc. 1988, 42(4), 556-558.
An analytical method that would enable the detection of low levels of
microorganisms in parenteral products without the need for incubation for
a long period of time would represent a significant advance in the
analysis of parenteral products. Such a method would preferably be used to
detect contamination by bacteria, yeast, or molds in drug vials and IV
bags.
SUMMARY OF THE INVENTION
The present invention comprises an analytical method based on near-IR light
intensity changes e.g., light scattering or absorption, as a method for
detecting small quantities of microorganisms in drug products e.g., in
sealed bags or vials. The method is, advantageously, noninvasive and
nondestructive, preventing possible contamination of bag or vial units by
the analytical method itself. In contrast, in prior art procedures
sterility testing and microbial identifications are accomplished by
looking at only a small number of units from the total lot of a product.
This is because these prior art microbiological tests are, time consuming,
laborious, invasive, and in essence destroy the product that is being
examined.
Near-IR light back-scattering is used in the present method for determining
low levels of contamination noninvasively and nondestructively. The method
is used to detect contamination by yeast, mold, and/or bacteria with a
detection limit potentially as low as three cfu of yeast per mL. Using the
near-IR method of the invention, each container, e.g. IV bag or vial, can
be evaluated intact with its sterility maintained, allowing the products
to be used or evaluated by another method.
The apparatus of the present invention includes a vial holder having a
substantially U-shaped mirror. The U-shaped mirror has a width
substantially corresponding to but slightly greater than the diameter of
the vial to be tested and held therein. The apparatus also includes a
near-IR light source that produces both incident and reference beams
having a wavelength of 800-2500 nm and, more preferably, 1100-1360 nm. The
light source is operatively connected by fiber optics to an integrating
sphere. The integrating sphere has both reference and incident beam ports
as well as a sample window directly opposite the incident beam port. A
detector is also mounted in the wall of the integrating sphere adjacent
the sample window. In operation, the incident beam is directed through the
sample window so as to enter the vial adjacent a sidewall of the mirror.
In this way, the U-shaped mirror reflects the incident beam so that it
passes through the vial and its contents three times before returning to
the detector. Any solid contaminants, such as microorganisms, present in
the liquid product held in the vial serve to scatter the incident beam.
The scattered light is collected by the inner reflective wall of the
integrating sphere and focused on the detector. Similarly, the reference
beam is also focused on the detector. Signal values from the detector are
then recorded and analyzed by computer and the resulting spectral patterns
are compared with patterns of known standards to determine the existence,
extent and/or type of contamination present.
DESCRIPTION OF THE DRAWINGS
FIG. 1 depicts 11 traces of the near infrared spectra of a PVC IV bag
containing 5% dextrose injection USP and 0.5 mg per mL of ranitidine as
the hydrochloride. The "log 1/R" indicates the logarithm of the reciprocal
of the reflectance intensity of the radiation.
FIG. 2 is a depiction of 11 separate spectra as set forth in FIG. 1 with
the exception that the IV bag of 5% dextrose solution USP and 0.5 mg per
mL ranitidine has been contaminated with Pseudomonas aeruginosa (ATCC No.
9027).
FIG. 3 is the most common appearance of a QQ plot of the training set (the
points on the left half of the plot) and the test set (the points on the
right half of the plot).
FIGS. 4, 5, and 6 depict the typical growth measured in cfu for a standard
bacteria, yeast and mold, respectively, in 150-mg/mL ranitidine as the
hydrochloride.
FIGS. 7 and 8 depict the spectra for PVC IV bags containing 5% dextrose and
0.5% aqueous dextrose without and with digital filtering, respectively.
FIG. 9 depicts near-IR spectra of IV bags containing 5% dextrose and 0.5
mg/mL ranitidine after mathematical preprocessing as described herein with
the upper trace being for a sterile bag and the bottom trace being for the
bag contaminated with bacteria.
FIG. 10 depicts 12 spectra from a bag contaminated with bacteria compared
to 11 spectra acquired from an uncontaminated bag.
FIG. 11 depicts the change in correlation of test bags as mold, yeast and
bacteria grow in the bag compared to their respective values immediately
after inoculation.
FIG. 12 schematically shows the apparatus of the present invention used to
collect spectral data for conducting contamination analysis of products in
vials.
FIG. 13 is a schematic showing of the training set and test set projection
process.
FIG. 14 is a quantile-quantile plot of two slightly different integrals,
the result of the test set being slightly smaller than the training set.
FIG. 15 is a spectra plot of control vials over time.
FIG. 16 is a spectra plot of Staphylococcus aureus vials over time.
FIG. 17 is a spectra plot of Pseudomonas aeruginosa vials over time.
FIG. 18 is a spectra plot of Pseudomonas cepacia vials over time.
FIG. 19 is a spectra plot of Escherichia coli vials over time.
Reference will now be made in detail to the drawing Figures.
DETAILED DESCRIPTION OF THE INVENTION
As used in the present specification, a near-IR spectrum is a spectrum of
the scattering (or reflectance) of light introduced into a liquid sample.
This is a physical phenomenon and is, in effect, a deflecting of the
incident near-IR light. Such is not an absorbance or transmittance
spectrum as in the more typical IR spectra which are indicative of the
individual chemical structural features, of a chemical compound. Since the
scattering method of the invention depends on the amount of scattered (or
reflected) light, any absorbance in the liquid sample will decrease the
quantity of light available for scattering.
The present invention comprises a method for the detection of
microorganisms in a liquid sample to be tested, which comprises the steps
of obtaining a near-IR spectrum of the liquid sample and then comparing
the spectrum to a standard sample. In more detail, the detection of
microorganisms will often be for the purpose of determining sterility (or
the lack of sterility) in the liquid sample. Examples of microorganisms
include any living cells which are individually not detected by visual
inspection. Specific examples include yeast, bacteria or mold. The liquid
sample to be tested is, in particular, water or an aqueous IV solution
such as a solution of dextrose, typically 5% (w/v), or isotonic sodium
chloride solution e.g., about 0.9% w/v. Other liquid samples that can be
evaluated according to the method of the present invention include aqueous
solutions used as growth media for fermentation stock or the growth of
other cells. In this case, one would detect the presence and quantity of
cells in order to determine whether or not there is sufficient population
of the cells for the purpose intended. In contrast, if the method of the
present invention were used to determine sterility, the object of the
exercise would be to confirm whether or not the liquid sample is sterile
as indicated by the absence of microorganisms.
Typical microorganisms to be detected according to the method of the
present invention include yeast, bacteria and mold. Other cells include
algae and other living cell lines such as cancer cell lines.
The infrared spectrum to be taken according to the method of the present
invention is a spectrum in the range of about 800-2500 nanometers (nm),
more particularly 1100-1360 nm. The spectrum can be taken on any
conventional near-IR spectrophotometer such as the InfraAlyzer 500 from
Bran+Leubbe of Elmsford, N.Y., the 6500 spectrophotometer from NIR Systems
of Silver Spring, Md. and the Quantum 1200 spectrophotometer from LT
Industries of Silver Spring, Md. In particular, the InfraAlyzer 500 can be
used according to the method of the present invention because it is a
double beam instrument and therefore need not be corrected for variations
such as fluctuations in source intensity.
The near-IR spectrophotometer utilized is configured to detect scattering
of the incident beam or changes in back-reflected light intensities
because of absorption processes. Detection of scattered or back-reflected
light from the incident beam can be accomplished by installing equipment
for light scattering as known in the art. For example, the EDAPT-1 probe,
available from Bran+Leubbe is suited for this purpose. Adaptation of
commercially available near-infrared spectrophotometers for the detection
of scattered light is described by Robert A. Lodder et al. in Appl.
Spectrosc. 1988, 42, 518-519 and in Appl. Spectrosc. 1988, 42(4), 556-558.
Other methods for detecting light scattering to be used in the method of
the present invention are those described in the chapter entitled
"Molecular Scattering Methods" in Spectrochemical Analysis by James D.
Ingle and Stanley R. Crouch, pp. 494-524, Prentice Hall, Englewood Cliffs,
N.J., 1988.
The liquid sample to be tested according to the invention is, in
particular, held in a container which is at least partially transparent to
at least one wavelength of near-infrared light. As the spectrophotometer
scans the near-infrared spectrum at those wavelengths wherein both the
liquid medium and the container holding the medium are at least partially
transparent, the spectrophotometer will then detect changes in the light
that passes through the container and medium and is reflected back or
scattered due to the presence of the microorganism to be detected.
Examples of the container include bags, bottles, tubes, vials and ampules
of glass (e.g., high grade borosilicate glass) or an organic polymer
(e.g., PVC, polyethylene and CR3 polymer from Abbott Laboratories,
Chicago, Ill.). In particular, the method of the invention can be used to
detect the sterility or loss of sterility, being more precise, of a liquid
for parenteral administration to humans. Examples of parenteral
administration include IV and intramuscular injections or irrigation of a
wound or other body cavity. The liquid medium may be composed of only a
fluid for administration or it may contain a pharmaceutical formulation
such as ranitidine hydrochloride injection. In addition to checking the
sterility of an aseptically refilled container, wherein a concern is the
growth of microorganisms, the method of the present invention can also be
used, conversely, to check the presence of such microorganisms that are
beneficial e.g., wherein one would want to check that the growth of
bacteria in a fermentation broth had been proceeding satisfactorily. A
particular application of the present invention is the determination of
sterility in an aseptically-filled container adapted for administration of
its contents to a human.
Once the spectra for the test sample and standard ("training") samples are
obtained, the spectra are compared. Comparison may be by visual inspection
of the spectra (e.g., in the range of 1100-1360 nm by measuring the log of
the reciprocal of reflectance). In general, several e.g., 10 spectra will
be taken for each sample at various locations through the container. The
trace of each of the spectra of the negative log of reflectance (or the
log of the reciprocal of reflectance) are considered. With a liquid sample
having an absence of microorganisms e.g., a sterile product, the various
traces of the spectra taken at different portions of the container will,
in general, have the same shape. In contrast, traces of a product with
microorganisms present will show different shapes for the spectra when
taken through different portions of the container. In fact, the traces
will often cross and such crossing is a good indicator of the presence of
microbial contaminants.
For example, in FIG. 1, one can see that the traces of spectra at various
portions of the sample are nearly identical to each other with the only
difference essentially being the position of the trace on the spectra as
caused by differences in the pathlength at the particular position at
which the spectrum was taken. In FIG. 2, one can see that contamination
caused by microorganisms (bacteria in this case) results in traces
crossing at various positions. This crossing phenomenon can be used to
determine the presence of microorganisms.
An analysis of the distribution quantiles of near-IR spectral data (Robert
A. Lodder et al. in Appl. Spectrosc. 1988, 42(8), 1512-1520) provides a
powerful means of interpreting light-scattering results. The principal
advantage of the near-IR light-scattering method is that every single unit
of the product can be examined for sterility without invading and
destroying the product. Furthermore, the method appears able to
differentiate between different types of microorganisms in solution as
well as to isolate the location of the organisms inside the container and
determine the number of microorganisms present.
The determination of microorganisms according to the invention is based
predominantly upon scattering of near-IR light by solid objects inside the
container, e.g. sealed IV bag or vial. Monochromatic near-IR light is
directed into the sample, and the solid material in the sample scatters
light back into an integrating sphere for collection and detection. A
fiber-optic diffuse-reflectance probe is used to collect spectral data
from a near-IR beam with a wavelength range from 1100-1360 nm. Light is
directed into the sample from a fiber-optic bundle that is placed in the
integrating sphere, e.g. a one-inch gold sphere directly opposite the
sample window (or beam port). A reference fiber-optic bundle is also
present to direct near-IR light into the integrating sphere (reference
beam). In particular, one may use such a pseudo-double-beam configuration
to compensate for noise caused by bending of the fiber and by source
intensity variations. Signal values are recorded as a ratio of intensities
between the sample and reference beams. The logarithm of the reciprocal of
the reflectance intensity recorded by this method is transmitted to a
computer such as a MicroVAX II for analysis.
EXAMPLE 1
Equipment. The spectrometer used to generate the near-IR light that was
transmitted through the optical fibers was in InfraAlyzer 500 scanning
spectrophotometer (Bran-Leubbe, Inc., Elmsford, N.Y.). The data were
actually collected on an IBM PS/2 model 50 computer (IBM Corp., Armonk,
N.Y.) running IDAS software (Bran+Leubbe). The collected reflectance
values were then transferred to a MicroVAX II computer system (Digital
Equipment Corp., Maynard, Mass.) and an IBM 3090-300E vector
supercomputer. Spectral data were processed in Speakeasy IV Epsilon
(Speakeasy Computing Corp., Chicago, Ill.) programs that were written
specifically for this purpose.
Materials. Thirty PVC IV bags containing 5 dextrose injections USP (Viaflex
150-mL containers, Lot# C092445, Baxter Healthcare Corp., Deerfield, Ill.)
were injected with 3-mL of Zantac injection 25 mg/mL ranitidine as the
hydrochloride, Glaxo Inc., Research Triangle Park, N.C.). Bags were
injected through the additive port with a sterile disposable syringe and
21G.times.1.5 in. needle (Becton Dickinson, Rutherford, N.J.). The nominal
ranitidine concentration in each bag was 0.5 mg/mL.
The microorganisms injected into the bags included: Candida albicans
(American Type Culture Collection number 10231), Aspergillus niger (ATCC
no. 16404), and Pseudomonas aeruginosa (ATCC no. 9027). These
microorganisms were chosen to include a species of yeast, mold, and
bacteria, respectively, which are typically tested to meet USP and FDA
requirements.
The inoculum was prepared by transferring the respective microorganism from
a lyophilized culture onto a solid agar medium and incubating at suitable
temperatures for sufficient growth. For Pseudomonas aeruginosa, Trypicase
Soy Agar was used, and the incubation time was 18-24 hours. Sabouraud
Dextrose Agar was used for Candida albicans and Aspergillus niger with
incubation times of 40-48 hours and 7 days, respectively. These agars and
incubation times are consistent with harvesting procedures for
pharmaceutical microbiological assays (see "Preparation of Inoculum",
Section <51>, USP XXII, United States Pharmacopeial Convention, 1989).
Cells were harvested into a sterile conical tube with 5% Dextrose Injection
USP instead of sterile saline TS to be consistent with the diluent used in
the IV bags. Cell concentrations for each species were adjusted to a
target range of 10-100 cfu per 0.10 mL (100-1000 cfu/mL) using 5% Dextrose
Injections USP. This range was selected to give a starting target
concentration of approximately 1 cfu/mL per bag, which represents a
reasonable contaminant load for a sterility violation. The number of cfu
per mL in the inoculum of each species was determined in quadruplicate by
the spread-plate method. The average inoculum concentrations from four
plates were 1650 cfu/mL, 100 cfu/mL, and 120 cfu/mL for Pseudomonas
aeruginosa, Candida albicans, and Aspergillus niger, respectively.
The additive port of each of the 30 bags was injected with 0.20 mL of
inoculum from one of the three microorganisms (10 bags of each type). The
bags were inverted several times to distribute cells throughout the bag.
Data Analysis. A spectral training set was constructed for each group of 10
bags containing a single variety of microorganism. The spectral training
set was collected immediately after injection of the microorganisms.
Spectra were also obtained from the bags before injection of the
microorganisms. These spectra, however, were not used as the training set
because the near-IR method appeared to detect the injection of medium and
microorganisms, which results in a large disturbance in the spectra. All
ten bags containing the same organisms were inoculated sequentially prior
to the training-set scans. The time lag between the scanning of the first
bag and tenth bag was approximately one hour. Furthermore, 12 scans over
the wavelength range from 1100-1360 nm were taken from each bag at
different portions of the bag. Therefore, each training set consisted of
10 IV bags containing one of three microorganisms in a 5% dextrose
solution with drug. Twelve scans were taken from each bag so that each of
the three training sets contained 120 spectral scans. During spectral
analysis, a spectrum recorded at 130 wavelengths in the 1100-1360 nm
region was projected as a single point in a 260-dimensional hyperspace.
Thus, each training set was composed of a cluster of 120 points in a
260-dimensional hyperspace. The analytical procedure located the center of
the training set from the spectra recorded at time-zero in a
260-dimensional space and integrated outward steadily in all directions in
space from the center of this training set to the "edges" of the
training-set cluster (the edges are defined typically as being three
multidimensional standard deviations away from the center). This integral
forms a function that is compared to a second integral, which is
determined by integrating from the center of the combined training set and
test set of spectra (where the test set is the spectra of the same group
of IV bags as the training set although at a later time, such as 6, 12, 24
or 48 hours after the injection of microorganisms). The 260-dimensional
points from these test-set bags scanned at the later times-are projected
into the same space as the training set spectra to form an augmented
spectral cluster. Integrating from the center of the augmented set out in
all directions at a constant rate produces a second integral. A plot of
the first integral versus the second integral is used to form a QQ plot.
Microorganism concentrations in bags 1 and 10 from each group were measured
at 0, 6, 12, 18 and 24 hours by removing 0.40 mL of solution from each bag
with a 0.5 mL syringe. A 0.10-mL aliquot (or diluted aliquot at high
microbiological concentrations) was transferred to each of four plates to
determine the average cell concentration in cfu/mL. Trypticase Soy Agar
was used as a growth medium for Pseudomonas aeruginosa aliquots, colonies
were counted after 48 hours at 30.degree.-35.degree. C. The results of the
counts are shown in FIG. 4. Sabouraud Dextrose Agar was used as the growth
medium for Candida albicans and Aspergillus niger aliquots, and colonies
were counted after 72 hours and 7 days, respectively, at
20.degree.-25.degree. C. These data are presented in graphical form in
FIGS. 5 and 6.
Calculations. Spectral data were collected at wavelengths N.sub.(m) ={1,2,
. . . ,w) on each sample bag. Treatment of collected spectral data I
begins with a smoothing process designed to reduce spectral noise:
I.sub.(1) =W(W(I)) eq 1
where W represents a linear smoothing operation in which i.sub.ij
=(i.sub.ij-2 +i.sub.ij-1 +i.sub.ij +i.sub.ij+1 +i.sub.1j+2)/5. Calculation
of the first derivative of the smoothed spectra removes baseline
variations from the spectra of the bag:
##EQU1##
A region of interest (i.e., a wavelength region where scattering is
expected to be observed from cells) is then selected in the spectra of
each bag. The region in this work encompasses one-third of the recorded
wavelength spectrum, leading to s.sub.(t) =[w/3]. A separate set of
derivative spectra is then calculated for the region of interest:
##EQU2##
The two spectra from each bag that show the most distinguishing spectral
features are selected by: The distinctive spectra, H.sub.(1) and
H.sub.(2), are combined by:
##EQU3##
P.sub.1 =M(I.sub.(s1),{1,2, . . . , u}) eq 6
P.sub.2 =M(I.sub.(s2),{1,2, . . . , u}) eq 7
H.sub.(1)j =I.sub.(1)p.sbsb.1.sub.j eq 8
H.sub.(2)j =I.sub.(1)p.sbsb.2.sub.j eq 9
The distinctive spectra, H.sub.(1) and H.sub.(2), are combined by:
H.sub.(1)j =H.sub.(1){w,w-1,w-2, . . . , 1} eq 10
.phi.=H.sub.(1)u -H.sub.(2)1 eq 11
H.sub.(2)j =H.sub.(2)j +.phi. eq 12
T.sub.i{1,2, . . . , u} =H.sub.(1)j eq 13
T.sub.i{u-1,u+2, . . . ,2u} =H(.sub.(2)j eq14
T.sub.i =W(T.sub.i) eq 15
to form an augmented spectral matrix that is useful in quantitative and
qualitative analysis. The augmented space T thus has d=2u dimensions
(columns) with one row for each sample bag.
Generally, another m-by-d matrix V, containing validation samples, is also
assembled from the same source as the training set and is likewise treated
in accordance to equations 1-15. The sample set V serves as an indicator
of how well the training set describes its overall population variation.
New spectra of sample bags under test are denoted X and are also treated
in accordance to equations 1-15 before quantitative or qualitative
analysis.
Bootstrap distributions are calculated by an operation .kappa.; and
.kappa.(T), .kappa.(X), and .kappa.(V) are each calculated in this manner.
The results are the m-by-d arrays B, B.sub.(x), and B.sub.(v). The
operation .kappa.(T), for example, begins by filling a matrix P with
sample numbers to be used in bootstrap sample sets B.sub.(s) :
P=P.sub.ij =r eq 16
The values in P are scaled to the training-set size by:
P=[(n-1)P+1] eq 17
A bootstrap sample B.sub.(s) is then created for each row i of the m-by-d
bootstrap distribution B by:
B.sub.(s) =t.sub.kj eq 18
where .kappa. are the elements of the i-th rows of P. The q-th row of B is
filled by the center of the q-th bootstrap sample,
##EQU4##
and the center of the bootstrap distribution is:
##EQU5##
The operation .kappa. is then repeated using X and V.
The multivariate data in the bootstrap distributions are then reduced to a
univariate form:
##EQU6##
and these distances are ordered and trimmed according to a trimming-index
set:
P(T)={mp+1, mp+2, mp+3, . . . , m-mp} eq 24
to reduce the leverage effects of isolated selections at the extremes of
the bootstrap distributions. Cumulative Distribution Functions (CDFs) for
QQ plotting are formed by:
C.sub.(t) =.differential.(S.sub.(T)P.sbsb.(T), S.sub.(T)P.sbsb.(T))eq 25
C.sub.(X) =.differential.(S.sub.(T)P.sbsb.(T), S.sub.(X)P.sbsb.(T))eq 26
C.sub.(V) =.differential.(S.sub.(T)P.sbsb.(T), S.sub.(V)P.sbsb.(T))eq 27
Graphing either C.sub.(x) or C.sub.(v) on the ordinate versus C.sub.(t) on
the abscissa produces a standard QQ plot. Patterns, in the QQ plot can be
used to analyze structure in the spectral data, and the significance of
the correlation between C.sub.(t) and C.sub.(x) can be used as an
indication of the existence of subclusters in the spectral data. In the
plot, a straight line with unit slope and an intercept of 0 indicates that
the two CDFs are essentially identical (this line should be observed when
C.sub.(v) is on the ordinate and C.sub.(t) is on the abscissa). The
presence of breaks in the line indicates that the CDF on the ordinate is
multimodal (i.e., that the test set and training set of samples are not
the same). Sharp bends in the QQ line also indicate the present of more
than one distribution in the CDF on the ordinate.
The Pearson Product Moment Correlation Coefficient between the two
integrals or CDFs is used as a means of quantifying the differences
between the test set and training set. The correlation between the two
integrals decreases steadily with time when an IV bag is contaminated. The
correlation coefficient can be used to provide both an indication of the
number of microorganisms present in a sealed container as well as how long
the microorganisms have been present in the container and what kind of
microorganisms are present in the container. The identification of
microorganisms is accomplished by preparing training sets of each type of
microorganism expected in the bag and projecting test spectra into a
training set space or library. Overlap should occur between the test group
and one of the groups in the training set library if the test bags are
contaminated with one of the microorganisms used to develop the training
set library.
Microorganism Growth. Each bag contained approximately 0.1 mg/mL phenol
because this preservative is present in the drug formulation of Zantac
Injection. Although this phenol level is insufficient to preserve the
bags, it is high enough to decrease organism growth rates. The slightly
elevated ambient temperature of the laboratory (30.degree.-35.degree. C.)
needed to facilitate operation of the near-IR spectrometer also had an
effect on microorganism growth. The high ambient temperature increased the
growth rate of Pseudomonas aeruginosa but decreased the growth rates of
Aspergillus niger and Candida albicans. These results were determined by
storing two duplicate bags for each microorganism at 20.degree.-25
.degree. C. and determining their growth-rate profile over 48 hours in a
similar manner. In all cases, solutions remained clear throughout the
course of the experiment with no visible signs of product contamination.
Near-IR Results. The baselines and peak heights of repetitive scans differ
somewhat because each spectrum was taken at a different location on the
Viaflex bag. Accordingly, the thickness of the plastic and aqueous sample
sampled can vary somewhat with each scan. Moreover, it is apparent from
looking at the near-IR spectra of water and the PVC plastic from the bag
that there are only a few relatively narrow spectral regions that may have
high sensitivity for looking at back-reflected or scattered light from
cells inside vials or bags. The regions around 1450 nm, 1940 nm, and 2500
nm are effectively obscured by intense water absorption. The PVC plastic
strongly absorbs around 1729 nm. Therefore, measurements of Near-IR light
returning through the water and the bag into the detector in the fiber
optic probe should be best in the 1100-1360 nm region, in a small region
around 1600 nm, and in the 2000-2400 nm region. Unfortunately, the
background absorption in the 2000-2400 nm region from water is still quite
high, so this region is virtually useless unless all of the material one
wishes to examine is adhered to the bag wall, which minimizes the amount
of water that the signal must pass through. The fact that water absorbs
more strongly in the "windows" around 1600 nm and 2200 nm than in the
window from 1100-1360 nm means that one should be able to determine the
location of microorganisms (a type of depth profiling) by looking at
spectral absorbances at 1100, 1600, and 2200 nm. For example, light
scattering from free-floating microorganisms should appear mainly at the
1100-1360 nm region. However, microorganisms adhering to the walls of the
container should appear at the 1600 and 2200 nm regions as well as in the
1100-1360 nm region. In fact, one might expect them to appear more
strongly in the 1600 and 2200 nm regions than the 1100-1360 region because
their absorption coefficients should be higher at the higher near-IR
wavelengths than at the lower near-IR wavelengths. Therefore, because the
pathlength for material adhered to the wall would be very limited, signals
for microorganisms adhering to the walls would be expected to be more
intense in the 1600 and 2200 nm wavelength regions. Spatial profiling can
also be accomplished with the near-IR method. A three-dimensional picture
of the contents of the bag can be roughly obtained if the bag is held
motionless and multiple scans are obtained by moving the fiber-optic
probe.
In a preliminary study, full spectral scans from 1100-2200 nm were obtained
for two inoculated bags. No significant absorbances were observed in the
1600 and 2200 nm regions, and it was believed that microorganisms injected
into the bags were floating freely in solution. This study was therefore
confined to the 1100-1360 nm region. FIG. 7 shows 12 spectra taken from a
single bag. These spectra are raw spectra and are not processed by any
filtering methods. They cover the entire spectral range from 1100-1360 nm.
The spectra-appear to be relatively noisy because very little light is
actually reflected back into the probe from the sample. During the first
few hours of cell incubation, few cells are in the solution and very
little contamination exists.
Spectra are filtered digitally and the 12 spectra taken from a single bag
are processed to combine them into a single spectrum. The process of
digitally filtering the spectra produces smooth and relatively noise-free
curves such as those shown in FIG. 8. The data in FIG. 8 come from 30
clean (i.e., uncontaminated), 150 mL, 5% dextrose bags containing drug. At
the early stages of contamination, it is important to move the probe
around the bag because light will be scattered and reflected back to the
probe only in a few locations where cells are present. The observation of
scattering during the first few hours of incubation appears to be somewhat
of a statistical phenomenon.
The mathematical problem is first one of identifying which of the 12
spectral scans from a bag actually show back-scattered light. Identifying
and quantifying the cells is then accomplished using these particular
scans. The first derivative is calculated for each of the 12 spectra and
the absolute value of the first derivative in the regions near 1100 nm and
1260 nm were examined more closely. The sum of the absolute values of the
first derivatives in these two regions was calculated for each of the 12
spectra, and the spectra showing the maximum sum were used to create a new
spectrum. If the same spectrum has the maximum absolute value of the first
derivative at both wavelengths, then it is the only spectrum selected, and
the resulting, noralized curve is symmetrical around the zero wavelength
displacement point.
FIGS. 1 and 2 demonstrate why this spectra preprocessing was necessary.
FIG. 1 shows 12 scans taken from an uncontaminated bag. The solid line
shows the curve with the maximum absolute value of the first derivative.
FIG. 2 shows 12 scans taken from a contaminated bag containing bacteria.
The solid line again shows the spectrum with the maximum absolute value of
the first derivative. It is evident that in clean bags, the major source
of spectral variation is a baseline variation that is predominantly
pathlength-dependent. In contaminated bags, however, certain spectra will
show large back scattering peaks that appear as dips in the spectra near
1100 nm and 1260 nm. Other scans on the contaminated bag will show no back
scattering at all. The value of the preprocessing technique for IV-bag
spectra becomes apparent when one examines FIG. 9, which shows scans for
both uncontaminated and contaminated IV bags. In FIG. 9, the displacement
value of zero represents the back scattering observed at 1100 nm. The
displacements that appear at 160 nm (both positive and negative) represent
scattered light observed at 1260 nm in the original spectra. In FIG. 9,
the lower curve is obtained from a contaminated bag while the upper curve
is obtained from a clean uncontaminated bag. The preprocessing and
filtering procedure is used to select the spectra that show the most back
scattering of light, and these spectra are transformed to principal axes
and used in the hyperspace integrating method. Integration of spectral
clusters in hyperspace begins with forming an estimate of the population
distribution in hyperspace from the existing training and test sets. This
estimate is formed by a bootstrap process.
The training set, test set, and validation set each have a CDF. The CDFs
for the training set, test set, and validation set are given by Equations
10, 11, and 12, respectively. Plotting the elements of the vector for the
training set on the abscissa versus the elements either of the test set or
of the validation set on the ordinate produces a standard QQ plot. When
two CDFs match, the result of the QQ plot is a straight line with a slope
of 1 and an intercept of 0. However, if the two CDFs are different, bends
or breaks appear in the line of the QQ plot. The presence of bends (where
two lines appear in the QQ plot with different slopes) indicates the
presence of two groups in hyperspace with different sizes. The presence of
a break in the QQ plot line indicates two groups in space centered at
different locations. The presence of both a bend and a break indicates
that two groups have different sizes and locations in multidimensional
hyperspace. Applying linear regressions to the points on the QQ plot
produces an equation whose linear coefficients have particular
significance. When the spatial volume of the test set is smaller than that
of the training set, the slope and intercept of the linear equation,
determined by regression, have values between 0 and 1. However, when the
volume of the test set is larger than that of the training set, the
coefficients of the straight line through the QQ plot tend to have a large
positive slope and a large negative intercept. Confidence limits are set
on the correlation between the two CDFs in the QQ plot. The confidence
limits are set through a bootstrap process similar to that used in
equations 1-5.
FIG. 10 depicts the projection of spectra of a clean bag (given by points)
and a contaminated bag (given by pluses) on a plane in multidimensional
hyperspace. The plane corresponds to that defined by the first and second
principal axes. FIG. 10 demonstrates that contaminated bags produce
spectral points in hyperspace that are more widely scattered than clean
bags. The larger spectral cluster of the contaminated bag occurs
presumably because its spectra are more variable. The spectra in FIG. 10
represent 12 scans taken at various locations on each of the two bags (one
clean bag spectrum with an A/D spike was eliminated). The fact that the
pulses from the contaminated bag do not overlap the cluster formed by the
points from the clean bag indicates that a spectral difference exists
between the clean bag and the dirty bag. The distance between the cluster
of points formed from spectra of the clean bag and the cluster formed from
the contaminated bag provides an indication of the amount of material that
is responsible for the contamination of the dirty bag. The direction of
the displacement from the center of the clean bag provides an identifying
spectrum of the material responsible for the contamination. Thus, distance
gives an indication of the number of microorganisms that are present in
the bag, while direction identifies the microorganisms present in the bag
that are responsible for the contamination.
FIG. 5 gives the growth curve for the yeast, Candida albicans. The assay
for Candida albicans was obtained through standard microbiological assay.
The same technique was employed to determine the concentrations of the
bacteria, Pseudomonas aeruginosa, and the mold, Aspergillus niger, that
appear in FIGS. 4 and 6, respectively. FIG. 11 is calculated from scans
that have been averaged for each of the 10 bags having the concentration
shown in FIGS. 4, 5 and 6. FIG. 11 is, in effect, a "growth curve" of
sorts measured by back scattering of near-IR light from the bags and/or
retroreflector. The 98% confidence limit on back scattering from clean
training set is given as the horizontal short-dashed line in FIG. 11 (the
level slightly above 0.93). The solid line represents the bacteria, the
dotted line represents the yeast, and the dashed-dotted line represents
the mold. After inoculation with 165 cfu per IV bag, the bacteria
(Pseudomonas aeruginosa) were observed at about six hours at an average
concentration of 57.+-.4 cfu/mL per bag. After injection of 10 cfu yeast
(Candida albicans) per bag, growth was detected at about four hours with
less than an average concentration of 3.+-.1 cfu/mL per bag. Injection of
the mold (Aspergillus niger) at a level of 12 cfu per bag allowed
detection of growth by near-IR spectrometry at about 24 hours at an
average concentration of 145.+-.20 cfu/mL per bag.
In FIG. 11, it appears that the yeast is the fastest growing species in the
bags. More specifically, about four hours after inoculation, the yeast bag
is seen to begin to lose correlation to its initial values indicating a
lack of sterility. For bacteria, this threshold appears to be about six
hours, whereas for mold, this threshold occurs at about 24 hours. The 98%
line indicates the correlation level at which bags can be considered to be
significantly different (statistically); two uncontaminated bags will
appear above this line 98 times out of 100.
In considering this data, it should be appreciated that yeast cells range
in size from 3-14 .mu.m and are larger than bacterial cells (about 0.5-2
.mu.m). At least initially, this size advantage might make yeast a better
source of light-scattering material than the bacteria, which actually
grows faster. Eventually the bacterial growth appears to overtake the size
advantage of yeast, and the bacteria then give the strongest
back-scattering signal. Mold is intermediate in size (approximately 3-8
.mu.m) and the slowest growing species as indicated by its continued
correlation change up to nearly 50 hours, while the correlation of the
yeast and bacteria seem to have begun to level off, presumably because of
the preservative (phenol) also present in the drug. What is most
noticeable in FIG. 11, however, is that even at six hours and below, the
near-IR method is able to detect contamination at a 98% confidence limit
for yeast and bacteria. FIGS. 4 and 5 indicate that neither the yeast nor
the bacteria have grown significantly after this short period of time.
Nevertheless, the near-IR method is still able to detect this
contamination. The correlation between spectral clusters at six hours is
poor for Candida albicans, Pseudomonas aeruginosa, and Aspergillus niger.
The poor correlation between the spectral clusters at all times (at six
hours and beyond) suggests that the near-IR method is able to
differentiate between these cell types as well as possible to provide an
indication of the extent of their growth.
Summary of Results. These data suggest that changes in near-IR spectra,
taken through the IV bags with a fiber optic probe and without product
tampering, correlate to organism growth. Moreover, spectra also
distinguish between bags contaminated with different classes of
microorganisms. Integration of the method of the invention with mechanical
techniques in product processing will allow an on-line sterility assurance
method in parenteral-production facilities, particularly for filling
processes e.g., aseptic-fill that require very careful control and
monitoring because of less than desirable assurance levels. The inability
in the prior art to test all parenteral units in an automated fashion is a
serious limitation to conventional microbiologic testing, particularly in
cases where microbial contamination is not distributed uniformly
throughout a batch (Henry L. Avallone, J. Parenter. Sci. Technol., 1985,
39(2), 75-79 and Henry L. Avallone, J. Parenter. Sci. Technol., 1986,
40(2), 56-57). It is very difficult, if not impossible, to detect a small
percentage of contaminated units within a large batch. Near-IR
spectrometry with a fiber-optic probe according to the present invention
can be used as an alternative or adjunct method to conventional
microbiologic testing in quality assurance and other applications where
large quantities of cells must be identified and quantified in a
relatively short period of time.
The variation in replicate spectra taken from the same IV bag is larger
than that observed with other containers because of the flexibility of the
PVC and poor near-IR transparency. To reduce the number of replicate scans
needed and to improve confidence statistics, further optimization and
improvements can be carried out in the sampling procedure, e.g.,
configuration of the optical probe, sample container and wavelength range
scanned.
In accordance with a further aspect of the present invention, an
alternative mathematical technique is used in conjunction with near-IR
spectroanalysis to successfully detect contamination of a product through
intact vials including those formed from glass. Once again, the evaluation
may be advantageously completed noninvasively and nondestructively without
sample preparation. Accordingly, the product is not destroyed.
Additionally, no contamination may possibly be introduced into the product
by the testing method. Hence, the tested product may subsequently be used.
More specifically, the alternative mathematical technique corrects for both
background and sample-matrix interferences utilizing a computerized
modeling process that is applied to the near-IR signals. Further, the
technique may not only be used to detect the contaminants Candida
albicans, Aspergillus niger and Pseudomonas aeruginosa but also other
bacteria including, for example, Staphylococcus aureus, Pseudomonas
cepacia and Escherichia coli.
In accordance with this alternative approach, the detection of cells in
drug solutions is also based on the ability of a spectrometer and a
computer to identify small changes in groups of spectra obtained from a
single unit of a product. To identify these changes successfully, spectra
recorded at d wavelengths are projected as single points in a
d-dimensional hyperspace. Spectral points obtained from sterile vials tend
to cluster in a small region of hyperspace. These points are designated
the training set T (see FIG. 12). When vials become contaminated, the
spectra of the contaminants cause a displacement in the position of the
spectral cluster X (obtained from contaminated vials) in hyperspace. In
addition, spectra obtained from the same vial tend to be less reproducible
when the vial is contaminated, leading to an increase in the volume of the
spectral cluster in hyperspace. Simultaneous changes in position and
volume of spectral clusters can be determined by comparing two integrals:
1. the integral of the training set T, from the center of T to the surface
of T, and
2. the integral of T, from the center to the surface, after T is augmented
by X.
In actuality, T and X are never precisely known, but rather are represented
by a discrete estimate of spectral points obtained from representative
vial samples. In order to increase the reliability of these estimates, all
spectra collected are filtered similarly to reduce the effects of noise on
the integral analysis.
The spectral filter is a function designated S that is used to represent
near-IR spectra in the form of smooth cubit splines that pass near, but
not through, the actual spectral data values:
T=S(W.sub.1, Y,W,t.sub.L,.delta.) eq 28
or
X=S(W.sub.1, Y,W,t.sub.L,.delta.) eq 29
S fits smooth curves constructed of cubic splines to the spectra Y(W).
W.sub.1 specifies the independent variable values (wavelengths) to which
interpolation is made. Y is made up of the dependent variable values
(absorbances or logarithms of reciprocal reflectances) that are to be
interpolated. W contains the independent variable (wavelength) values
corresponding to Y, and is generally (though not necessarily) the same as
W.sub.1. The scalar t.sub.L is a tolerance value that controls the extent
of smoothing, and is the acceptable root mean square relative deviation of
the fitted curve:
RMS(Z).ltoreq.t.sub.L, where z.sub.j =F(w.sub.j -y.sub.j)/.delta..sub.j.eq
30
.delta. is an array of the estimated errors (is SDs) in the absorbance
values Y. The smoothing splines are of the form:
F(W)=A.sub.j.DELTA. +Bj.DELTA.+C.sub.j.DELTA. 2+Dj.sub..DELTA. 3eq 31
where w.sub.j .ltoreq.w.ltoreq.w.sub.j+1 [1.ltoreq.j.ltoreq.m-1],
.DELTA.=w-w.sub.j, and m is the number of wavelengths in the W and Y
arrays. Note that W.sub.1 may have a different number of wavelengths
(columns) than W and Y. The resulting filtered spectral set (either T or X
or both) has as many columns as W.sub.1, and as many rows as the number of
spectra to be smoothed (i.e. the number of rows of Y).
The BEST is a flexible clustering procedure that is applied to the smoothed
spectra. Extending the method to search for subclusters within a training
set requires a filtered training set T and test set X, as well as the
calculation of these sets' respective bootstrap distributions, B and
B.sub.(X). The discussion that follows outlines one route to a solution to
the subcluster-detection problem.
A training set of sample spectral values (e.g., reflectance or absorbance),
recorded at d wavelengths from n uncontaminated vials, is represented by
the n-by-d matrix T. (Often, another n-by-d matrix V, containing
validation vials, is also assembled from uncontaminated vials like the
training set. The sample set V serves as an indicator of how well the
training set T describes the overall population variation of spectral
values obtained from uncontaminated vials.)
The second step of the basic BEST calls for the calculation of bootstrap
distributions. Bootstrap distributions can be calculated by an operation
.kappa.; .kappa.(T), .kappa.(X), and .kappa.(V) are each calculated in
this manner. The results of the .kappa. function are the m-by-d arrays B,
B.sub.(X), and B.sub.(V).
The operation .kappa.(.tau.) begins by filling a matrix P with sample
numbers to be used in bootstrap sample sets B.sub.(s) :
P=p.sub.ij =.tau.. eq 32
The values in P are scaled to the training-set size by:
P=[(n-1)P+1] eq 33
A bootstrap sample is then created for each row i of the m-by-d bootstrap
distribution B by
B.sub.(s) =t.sub.kj eq 34
where .kappa. are the elements of the i-th rows of P. The q-th row of B is
filled by the center of the q-th bootstrap sample
##EQU7##
and the center of the bootstrap distribution is
##EQU8##
The operation .kappa. is then repeated using the vial spectra in X and V.
The multivariate data in the bootstrap distributions are then reduced to a
univariate form:
##EQU9##
and these distances are ordered and trimmed according to a trimming-index
set
P.sub.(T) ={mp+1, mp+2, mp+3, . . . , m-mp} eq 40
to reduce the leverage effects of isolated selections at the extremes of
the bootstrap distributions. A hypercylinder can be formed about the line
connecting C to the center of B.sub.(X) or B.sub.(V), giving directional
selectivity to the information in S.sub.(T), S.sub.(X), and S.sub.(V) if
desired. S.sub.(T), S.sub.(X), and S.sub.(V) then have n.sub.h elements
instead of m elements. Subclusters can be detected without this
selectivity, however. While this directional selectivity adds to the
sensitivity of the subcluster test, it also introduces a number of
additional questions, e.g.: How small a radius is too small? How many
replicates are required for a given radius? At what point is the
additional sensitivity merely reacting to the particular training set
selected, and not to any population characteristic? The validation of the
spectrometric method for analysis of vials is complicated by the
introduction of the hypercylinder, and the results of the method do not
appear to be superior when the hypercylinder construct is applied to
spectra obtained from the vials. Thus, the hypercylinder implementation of
the BEST is not employed in the analysis of the vials.
Instead, cumulative distribution functions (CDFs) for quantile-quantile
plotting are formed by:
C.sub.(t) =.differential.(S.sub.(T)P.sbsb.(T), S.sub.(T)P.sbsb.(T))eq 41
C.sub.(X) =.differential.(S.sub.(T)P.sbsb.(T), S.sub.(X)P.sbsb.(T))eq 42
C.sub.(V) =.differential.(S.sub.(T)P.sbsb.(T), S.sub.(X)P.sbsb.(T))eq 43
Plotting the elements of C.sub.(T) on the abscissa versus the elements of
either C.sub.(X) or C.sub.(V) on the ordinate produces a standard
quantile-quantile (QQ) plot (2). Patterns in such a plot can be used to
analyze structure in the spectral data obtained from the vials, and the
significance of the correlation between C.sub.(T) and C.sub.(X) can be
used as an indication of the existence of subclusters in the spectral data
that reflect contamination in the vials. In the QQ plot, a straight line
with unit slope and an intercept of 0 indicates that the two cumulative
distribution functions are essentially identical (this should be observed
when C.sub.(V) is on the ordinate, or when the test vial is
uncontaminated). In the extended BEST QQ plot, the presence of breaks in
the line indicates that the CDF on the ordinate is multimodal (i.e., that
the test spectra and training spectra are not the same, and the test vial
is contaminated). Sharp bends in the QQ line also indicate the presence of
more than one distribution in the CDF on the ordinate, and indicate the
presence of contamination.
The apparatus 10 of the present invention as shown in FIG. 12 includes a
vial holder 12. The vial holder 12 may, for example, be formed of plastic
and has a substantially U-shaped cavity 14. The cavity 14 has a width
substantially corresponding to but just slightly larger than the diameter
of the vial V to be tested. The U-shaped wall of the cavity 14 is lined
with aluminum so as to provide a substantially U-shaped mirror 16.
The vial holder 12 advantageously serves to reduce stray light that would
otherwise enter the apparatus 10 through the vial V and its contents. In
certain situations this stray light could adversely effect contamination
analysis by leading to false readings. Hence, this problem is
significantly reduced. Additionally, as described in greater detail below,
the U-shaped mirror 16 is also adapted to reflect the incident beam I
several times through the vial V and the liquid product contained therein.
Advantageously, this seems to significantly enhance the sensitivity of the
apparatus 10 with respect to the detection of microorganisms.
The apparatus 10 also includes a near-infrared light source 18, such as in
InfraAlyzer 500. The light source 18 is adapted to produce an incident
beam I and a reference beam R of light having identical wavelength
characteristics. The wavelength of light utilized to perform the analysis
falls in a range of 800-2500 nm and, more preferably, 1100-1360 nm.
As shown, the apparatus 10 also includes an integrating sphere 20 such as a
gold sphere having a one inch diameter. The integrating sphere 20 includes
an incident beam port 22, a reference beam port 24 and a sample window 26
directly opposite the incident beam port. The reference beam R is carried
from the light source 18 to the reference beam port 24 by means of a fiber
optic bundle 28. Similarly, the incident beam I is carried from the light
source 18 to the incident beam port 22 by means of a fiber optic bundle
30.
The reference beam R serves to compensate for noise caused by the bending
of the fiber optic bundles 28, 30 and/or by source intensity variations.
The reference beam R is directed through the port 24 so as to strike the
inner reflective wall of the integrating sphere 20 and be reflected back
toward a spectral detector 32. One such detector 32 that may be utilized
for this purpose is an EDAPT-1 probe.
The incident beam I is directed from the port 22 through the sphere 20 and
sample window 26 into the vial V and the liquid product contained therein.
The incident beam I is then reflected by the mirror 16 at the point P, so
as to pass again through the vial V and the product. Next, the incident
beam I is again reflected by the mirror 16 at the point P.sub.2 so as to
pass for a third time through the vial V and the product. Finally, the
incident beam I is reflected by the mirror 16 at the point P.sub.3 back
through the sample window 26 to the detector 32.
Any light of the incident beam I striking any solid contamination such as a
microorganism; during the three passes through the vial V as described
above is scattered, thereby changing the spectra. Scattered light
reflected back through the sample window 26 is integrated/collected by the
inner reflective wall of the sphere 20 and focused upon the detector 32.
Changes in the spectra that could result from other causes such as
incomplete transmission through the vial V or absorption by molecules in
the solution are compensated for by comparing the actual spectra obtained
to sample spectra of known standards or controls during subsequent
analysis.
Signal values from the detector 32 are transmitted along a control line 34
to an analyzer 36. More specifically, the signal values are recorded as a
ratio of intensities between the incident beam I and reference beam R. The
logarithm of the reciprocal of the reflectance intensity is then used to
complete the analysis by means of a computer such as described below in
Example 2.
The first step is the placing of a vial V to be tested into the cavity 14
of the vial holder 12 so that the wall of the vial is adjacent the
U-shaped mirror 16. A scanning plane is then selected and the integrating
sphere 24 is positioned so as to align the sample window 26 with the
selected scanning plane. When properly positioned along the open end of
the mirror 16, the scanning window 26 extends lengthwise substantially
between the median line M and outer edge E of the vial V adjacent the
mirror (see FIG. 12). As shown by the incident beam line I in this drawing
figure this positioning insures that the incident beam is directed through
the vial V and its contents three times before being reflected back to the
detector 32.
Any solid contaminants, such as microorganisms, present in the liquid
product in the vial V in the scanning plane serve to scatter the light of
the incident beam I. Portions of such scattered light pass back through
the sample window 26 into the integrating sphere 20. The reflective wall
of the integrating sphere serves to collect and focus the scattered light
on the detector 32. Similarly, the reference beam R is reflected by the
inner wall of the sphere 20 onto the detector 32. The detector 32 produces
signals which are communicated along the control line 34 to a computer
analyzer 36. Several other different scanning planes are also selected and
spectral readings recorded. For example, up to ten or more scanning planes
and readings may be recorded for a single vial. The resulting readings are
then analyzed by the computer 36 and are also compared to known standards.
This allows not only the existence of contamination to be determined but
in many cases the extent and type of contamination as well.
EXAMPLE 2
Equipment. Near-IR energy was transmitted through the apparatus 10
described above using an InfraAlyzer 500 scanning spectrophotometer
(Bran+Luebbe). Data was collected on an IBM PS/2 model 50 computer (IBM
Corp., Armonk, N.Y.) running IDAS software (Bran+Leubbe). Collected
reflectance values were then transferred to a MicroVAX II computer system
(Digital Equipment Corp., Maynard, Mass.) and an IBM 3090-300E vector
supercomputer. Spectral data was processed for analysis in accordance with
the mathematical technique set forth in equations 28-43 using Speakeasy IV
Epsilon and Zeta (Speakeasy Computing Corp., Chicago, Ill.) programs that
were written specifically for this purpose (That is: BEST).
Materials. Thirty glass vials containing a nutrient medium were divided
into 5 groups of six vials each. One group of six vials served as control
vials, and were injected with nutrient medium. The vials in the other four
groups were injected with a different species of bacteria for each group.
The vials were injected through the rubber cap with a sterile disposable
syringe and 21G.times.1.5 in. needle (Becton Dickinson, Rutherford, N.J.).
The microorganisms injected were: Staphylococcus aureus (American Culture
Collection number (ATCC no. 6538), Pseudomonas cepacia ATCC no. 25416),
Escherichia coli (ATCC no. 8677), and Pseudomonas aeruginosa (ATCC no.
9027). These microorganisms were chosen to include a spectrum of bacteria
that must be monitored to meet USP and FDA requirements.
Inoculum was prepared by transferring the respective microorganisms from a
lyophilized culture onto a solid agar medium and incubating at suitable
temperatures for sufficient growth. For Pseudomonas aeruginosa, Trypicase
Soy Agar was used, and the incubation time was 18-24 hours. The agar and
incubation time is consistent with harvesting procedures for
pharmaceutical microbiological assays.
Cell concentrations were adjusted to a target range of 10-100
colony-forming units (cfu) per 0.10 mL using Trypicase Soy Broth. This
range was selected to give a starting target concentration of less than 1
cfu per bag, which represents a reasonable contaminant load for a
sterility violation. The number of cfu per mL in the inoculum was
determined in quadruplicate by the spread-plate method. The averages from
four plates were 12.0 cfu/mL, 120.0 cfu/mL, 444.0 cfu/mL, and 12.0 cfu/ml
for Staphylococcus aureus, Pseudomonas aeruginosa, Escherichia coli, and
Pseudomonas cepacia, respectively.
The cap of each of the 30 vials was injected with 0.10 mL of inoculum from
one of the four microorganisms (6 vials of each type). The vials were
inverted several times to distribute the cells throughout the vials.
Data Analysis. All six vials containing the same organism in each group
were inoculated sequentially prior to collecting the training-set scans.
The time lag between the scanning of the first vial and the sixth vial was
approximately 30 minutes. Furthermore, 10 scans of the wavelength range
from 1100-1360 nm were taken from each vial at slightly different
positions on the vial. During spectral analysis, a spectrum recorded at
130 wavelengths in the 1100-1360 nm region was projected as a single point
in a 130-dimensional-hyperspace. The analytical procedure located the
center of the training set from the spectra recorded at time-zero in a
130-dimensional space and integrated outward steadily in all directions in
space from the center of this training set to the "edges" of the
training-set cluster (the edges are defined typically as being three
multidimensional standard deviations away from the center). This integral
forms a function that is compared to a second integral, which is
determined by integrating from the center of the combined training set and
test set of spectra (where the test set is the spectra of the vials at a
later time, such as 6, 12 or 18, 24 or 48 hours after the injection of
microorganisms). A plot of the first integral versus the second integral
is used to form a QQ plot. FIG. 13 is a schematic diagram of the training
set and test set projecting process. FIG. 14 is a QQ plot of two slightly
different integrals that result from the projection of a test set into an
augmented-set space when the test set is slightly different from the
training set (here, the test set is slightly smaller in volume than the
training set).
After scanning vials 1 through 6 from each group at 1, 6, 12 or 18, 24, and
48 hours, microorganism concentrations in vials 1 and 6 were measured by
removing 0.40 mL of solution from each vial with a 0.5 mL sterile syringe.
A 0.10-mL aliquot (or diluted aliquot at high concentrations) was
transferred to each of four plates to determine the average cell
concentration in cfu/mL. Trypticase Soy Agar was used as a growth medium
for the Pseudomonas aeruginosa aliquots, and colonies were counted after
48 hours at 30.degree.-35.degree. C. The results for each microorganism
(cfu/mL) are shown in the third column of Table 1.
Summary of Results. A total of 30 vials were prepared containing sterile
nutrient media. Six of these vials were injected with additional sterile
media and served as control vials over the 48 hours in which spectra were
collected from all 30 vials. Six vials were injected with Staphylococcus
aureus, six with Pseudomonas aeruginosa, six with Pseudomonas aeruginosa,
and six with Escherichia coli. Each of the 30 vials were scanned 10 times
immediately following injection, and these spectra are depicted in FIGS.
15-19 as the time=0 point. The vials were allowed to incubate at room
temperature for two days. During this 2-day period 10 spectra were
collected from each of the vials at 6, 12 or 18, 24, and 48 hours after
injection. Integrals calculated from the center of a cluster of spectral
points at time=0 were correlated to integrals calculated at a later time.
The results of these correlations are presented in FIGS. 15-19. The solid
line in each of these figures represents the average correlation of 6
replicate analyses of vials containing a certain type of injection
(control injection or bacterial injection). The dashed lines represent
.+-.1 error bars on the average correlation. The dot-dashed line signifies
a 98% confidence limit on the time=0 spectra of a given injection type
(i.e., 98% of the time, the correlation between 2 sets of spectra obtained
from time=0 spectra of the given injection is higher than this value).
FIG. 15 depicts the change in spectra of the control vials with time. The
spectra of the control vials do not change significantly (at the 98%
level) over the 48-hour period.
FIG. 16 shows the change in the spectra of the vials injected with
Staphylococcus aureus with time. The vials cross the 98% confidence limit
between 11 and 12 hours after injection of 10-100 cfu.
FIG. 17 graphs the change in spectra of the Pseudomonas aeruginosa vials
with time. These vials cross the 98% level about 19 hours after injection
with 10-100 cfu. Interestingly, the spectra begin to resemble the time=0
spectra again after approximately 43 hours. The rise in Pseudomonas
aeruginosa correlations is similar to that reported for Pseudomonas
aeruginosa in PVC administration bags (Example 1). It appears that the
growth rate of Pseudomonas aeruginosa is different in vials; however, this
difference in growth is probably due to differences in the media used in
the vial and bag studies and to differences in the optical collection
efficiency.
FIG. 7 shows the change in the spectra of Pseudomonas cepacia vials with
time. The vials cross the 98% level initially about 8 hours after
injection of 10-100 cfu. The correlation does not continue to fall with
time, however, and the vials begin to resemble the time=0 spectra again
after 20 hours. However, after 34 hours the vial spectral cross the 98%
confidence level again. This double-crossing of the confidence limit was
also observed in E. coli vials (see FIG. 8). E. coli crossed the 98%
confidence level initially approximately 12 hours after injection of
10-100 cfu. The vials resembled the time=0 spectra for a short period from
22 to 26 hours after the injection of bacteria. Beyond 26 hours after
injection the vials apparently stayed below the 98% correlation confidence
level.
In a related study employing Candida albicans in PVC administration bags,
the correlation of spectra to time 0 values was monitored continuously,
and the first time the correlation dropped below the 98% confidence level,
the media was withdrawn from the bags into new bags through a 0.2 .mu.m
filter. Fresh media was then backflushed into the original bags, carrying
with it all of the solid material from the filter. In this way, the solid
material (cells, etc.) from the original bags was separated from the
solution. When the solid material bags were compared to the time=0 bag
spectra, the bags containing the cells and solid material were identical
at the 98% level to the time=0 bags. The bags containing the solution from
the original bags, however, were different from the time=0 bag spectra at
the 98% level, indicating that the earliest changes observed in bag
spectra are due to changes in the composition of the solution in the bags,
and not to an increase in scattered light from cells in solution.
The double-crossing of the 98% confidence level may therefore represent a
change in the physical mechanism at the source of the spectral changes
observed by the near-IR macroscopic technique. The first crossing may
occur when changes in the composition of the solutions alter the
transmission spectra of the bags. As the number of cells increases in the
bags, the number of scattering events per unit volume increases,
shortening the average pathlength and weakening the effect of solution
changes on the observed spectra, (hence, the spectra recorded at later
times may begin to resemble the spectra recorded at time=0). Finally, as
cells continue to proliferate, the spectral contribution of light
scattered by cells may become significant in the observed spectra, and the
drop in correlation between time=0 spectra and later spectra may being
again. These results indicate that the present near-IR method of vial
inspection is able to detect changes in the composition of sealed vials.
______________________________________
Abbreviations
IV intravenous
PVC polyvinyl chloride
mL milliliters
nm nanometers
QQ quantile-quantile
CDF Cumulative Distribution Functions
near-IR near infrared radiation
cfu colony forming units
mg milligrams
FDA U.S. Food and Drug Administration
USP United States Pharmacopeia
ATCC American Type Culture Collection
hrs hours
eq equation
LIST OF SYMBOLS
Special defined operations:
W linear ("moving average") smoothing
d(f(x))/dx derivative of f(x)
--M(f(x),x)
x (d(f(x))/dx) = 0 .LAMBDA. (d.sup.2 (f(x))/dx.sup.2) < 0
r random number on 0 < x < 1; Monte Carlo
integration of continuous uniform
distribution
.kappa.(Z) creates a bootstrap distribution
containing m elements for a set of real
samples, and find the center of this
bootstrap distribution
[x] the greatest-integer function of a scalar,
matrix, or array
.differential.(x)
ordered elements of x (x is a matrix or
array)
= equals, or "is replaced by" when the same
variable appears on both sides of =
.sub.- s spectral estimator (filter) based on cubic
splines
Scalars:
n the training-set, test-set, and
validation-set size, i.e., the number of
samples that the set contains
d the number of wavelengths and the
dimensionality of the analytical space
m the number of sample set replications
forming a bootstrap distribution (user-
determined)
i an index for counting rows in a matrix or
array
j an index for counting columns in a matrix
or array
n.sub.h the number of replicate spectral points
falling inside a hypercylinder
p proportion of a distance distribution to
trim from each end of the distribution
t.sub.L tolerance value on spectral estimation
.DELTA. wavelength increment
.tau. random number on interval
[0 .ltoreq. .tau. < 1]
u the number of spectra collected from a
single sample bag
w the number of wavelengths collected from
a single sample bag
s.sub.t an index marker for a wavelength region of
interest
p.sub.1 the index number of a spectrum showing the
greatest overall signal in a set of u
spectra
p.sub.2 the index number of a spectrum showing the
greatest analytical signal over a
wavelength region of interest in a set of
u spectra
.phi. the difference between the absorbances of
two spectra from a single bag at the
lowest wavelength
Matrices, vectors, and arrays:
N.sub.(m) = (n.sub.(m)j).sub.w
wavelength vector recorded by
spectrometer
I.sub.(dl) = (i.sub.(dl)ij).sub.u,w
first derivatives of all spectra
collected from a single bag
I.sub.(1) = (i.sub.(1)ij).sub.u,w
smoothed set of u spectra collected
from a single bag
I = (i.sub.ij).sub.u,w
set of u spectra as collected from a
single bag
I.sub.(d2) = (i.sub.(d2)ij).sub.u,w-s.sbsb.t
first derivative of region of
interest
I.sub.(s1) = (i.sub.(s1)i).sub.u
sum of absolute value of first
derivative of full spectra
I.sub.(s2) = (i.sub.(s2)i).sub.u
sum of absolute value of first
derivative of wavelength region of
spectral interest
H.sub.(1) = (h.sub.j).sub.w
spectrum selected by the index p.sub.1
H.sub.(2) = (h.sub.j)
spectrum selected by the index p.sub.2
B = (b.sub.ij).sub.m,d
m-by-d bootstrap distribution of
training-set sample spectra
B.sub.(X) = (b.sub.ij).sub.m,d
bootstrap distribution of test-set
sample spectra
B.sub.(V) = (b.sub.ij).sub.m,d
bootstrap distribution of validation-
set sample spectra
C - (c.sub.j).sub.d
center of the bootstrap distribution
B
P = (p.sub.ij).sub.m,n
training-set sample numbers selected
for the bootstrap-sample sets used to
calculate bootstrap distribution
W = (w.sub.j)d
wavelength at which signals are
recorded
W.sub.i = (w.sub.ij)d
wavelengths at which signals are
estimated by filter -S
Y = (y.sub.ij).sub.n,d
signals recorded from n vials at d
wavelengths
.delta. = (.delta..sub.ij).sub.n,d
estimated errors (in SDs) of the
signals recorded in Y
T = (t.sub.ij).sub.n,d
training-set sample spectra
X = (x.sub.ij).sub.n,d
test-set sample spectra
V = (v.sub.ij).sub.n,d
validation-set sample spectra
K = (k.sub.j).sub.n
training-set sample numbers selected
for a particular bootstrap sample
B.sub.(s) = (b.sub.(s)ij).sub.n,d
bootstrap sample set used to
calculate single rows of a bootstrap
distribution
(B, B.sub.(X), or B.sub.(V))
S.sub.(T) = (s.sub.(T)i).sub.m
Euclidean distances of training-set
replicates from C, the center of the
bootstrap distribution of the
training set
S.sub.(X) = (s.sub.(X)i).sub.m
Euclidean distances of test-set
replicates from C
S.sub.(V) = (s.sub.(V)i).sub.m
Euclidean distances of validation-
set replicates from C
P.sub.(T) = (p.sub.i).sub.m-2pm
set of (m-2pm) indices used for
trimming distance distributions
C.sub.(t) = (c.sub.(t)i).sub.2m-4pm
cumulative distribution function
(CDF) formed by the trimmed and
ordered elements of the training-set
bootstrap distribution; CDF has (2m-
4pm) elements
C.sub.(X) = (c.sub.(X)i).sub.2m-4pm
CDF formed by the trimmed and ordered
elements of the test-set and
training-set bootstrap distributions
C.sub.(V) = (c.sub.(V)i).sub.2m-4pm
CDF formed by the trimmed and ordered
elements of the validation-set and
training-set bootstrap distributions
______________________________________
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